可解释的医学图像分类：利用原型学习和特权信息
Interpretable Medical Image Classification using Prototype Learning and Privileged Information
AI总结：
本研究提出Proto-Caps模型，结合胶囊网络、原型学习和特权信息，提升医学图像分类的可解释性和预测性能，准确率超过现有方法6%。
摘要：
可解释性往往是医学影像中的关键要求。先进的深度学习方法被要求解决这一需求，以实现可解释性和高性能。在本工作中，我们研究是否可以利用训练过程中额外的信息来创建一个可理解且强大的模型。我们提出了一种创新的解决方案，称为Proto-Caps，它结合了胶囊网络、原型学习和特权信息的益处。在LIDC-IDRI数据集上评估所提出的方法表明，它在可解释性和预测性能方面均优于现有方法。与可解释性基线模型相比，我们的方法在预测恶性（93.0%）和肺结节的平均特征方面实现了超过6%的准确率提升。同时，模型提供了基于案例的推理，通过原型表示允许对放射科定义的属性进行可视化验证。
英文摘要：
Interpretability is often an essential requirement in medical imaging. Advanced deep learning methods are required to address this need for explainability and high performance. In this work, we investigate whether additional information available during the training process can be used to create an understandable and powerful model. We propose an innovative solution called Proto-Caps that leverages the benefits of capsule networks, prototype learning and the use of privileged information. Evaluating the proposed solution on the LIDC-IDRI dataset shows that it combines increased interpretability with above state-of-the-art prediction performance. Compared to the explainable baseline model, our method achieves more than 6 % higher accuracy in predicting both malignancy (93.0 %) and mean characteristic features of lung nodules. Simultaneously, the model provides case-based reasoning with prototype representations that allow visual validation of radiologist-defined attributes.
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